Table REF  presents the results obtained over Semeion Handwritten Digit dataset, being IHS and JADE the most accurate techniques. The best results concerning MNIST and Semeion Handwritten Digits datasets, as can be clearly seen on Tables REF  and  REF , was acquired using the DBM. DBN, however, had the best results considering CalTech 101 Silhouettes dataset, as presented in Table REF . Some interesting conclusions can be extracted from a closer look at these results: (i) meta-heuristic-based optimization allows more accurate results than a random search, as argued by the works of Papa et al. [1]}, [2]}, [3]} already; (ii) DBMs seem to produce more accurate results than DBNs; (iii) the number of layers do not seem to influence the results when one fine-tune parameters; (iv) IHS achieved the best results in all datasets (concerning both DBN and DBN), but with results statistically similar to other meta-heuristic techniques as well; and (v) we could not realize a significant difference between CD and PCD, since we employed 10 iterations for learning only. Actually, PCD is expected to work better, but at the price of a longer convergence process.
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